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E-grāmata: Advances in Battery Manufacturing, Service, and Management Systems

Edited by (University of Wisconsin-Madison, USA), Edited by (University of Wisconsin-Madison, USA), Edited by (University of Wisconsin-Madison, USA)
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Addresses the methodology and theoretical foundation of battery manufacturing, service and management systems (BM2S2), and discusses the issues and challenges in these areas

This book brings together experts in the field to highlight the cutting edge research advances in BM2S2 and to promote an innovative integrated research framework responding to the challenges. There are three major parts included in this book: manufacturing, service, and management. The first part focuses on battery manufacturing systems, including modeling, analysis, design and control, as well as economic and risk analyses.  The second part focuses on information technologys impact on service systems, such as data-driven reliability modeling, failure prognosis, and service decision making methodologies for battery services. The third part addresses battery management systems (BMS) for control and optimization of battery cells, operations, and hybrid storage systems to ensure overall performance and safety, as well as EV management.  The contributors consist of experts from universities, industry research centers, and government agency. In addition, this book:





Provides comprehensive overviews of lithium-ion battery and battery electrical vehicle manufacturing, as well as economic returns and government support Introduces integrated models for quality propagation and productivity improvement, as well as indicators for bottleneck identification and mitigation in battery manufacturing Covers models and diagnosis algorithms for battery SOC and SOH estimation, data-driven prognosis algorithms for predicting the remaining useful life (RUL) of battery SOC and SOH Presents mathematical models and novel structure of battery equalizers in battery management systems (BMS) Reviews the state of the art of battery, supercapacitor, and battery-supercapacitor hybrid energy storage systems (HESSs) for advanced electric vehicle applications

Advances in Battery Manufacturing, Services, and Management Systems is written for researchers and engineers working on battery manufacturing, service, operations, logistics, and management. It can also serve as a reference for senior undergraduate and graduate students interested in BM2S2.
Preface xv
Contributors xix
Part I Battery Manufacturing Systems
1 Lithium-Ion Battery Manufacturing For Electric Vehicles: A Contemporary Overview
3(26)
Wayne Cai
1.1 Introduction
3(1)
1.2 Li-Ion Battery Cells, Modules, and Packs
4(4)
1.2.1 Formats of Li-Ion Battery Cells
6(2)
1.2.2 Battery Modules and Pack
8(1)
1.3 Joining Technologies for Batteries
8(11)
1.3.1 Ultrasonic Metal Welding
9(6)
1.3.2 Resistance Welding
15(1)
1.3.3 Laser Beam Welding
15(3)
1.3.4 Wire Bonding
18(1)
1.3.5 Mechanical Joining
19(1)
1.3.6 Summary
19(1)
1.4 Battery Manufacturing: The Industrial Landscape
19(6)
1.4.1 Cell Manufacturing
19(4)
1.4.2 Module Assembly (Cell-to-Cell)
23(1)
1.4.3 Pack Assembly (Module-to-Module)
24(1)
1.5 Conclusions
25(1)
References
25(4)
2 Improving Battery Manufacturing Through Quality And Productivity Bottleneck Indicators
29(28)
Feng Ju
Jingshan Li
Guoxian Xiao
Ningjian Huang
Jorge Arinez
Stephan Biller
Weiwen Deng
2.1 Introduction
29(2)
2.2 Literature Review
31(2)
2.3 Problem Formulation
33(2)
2.4 Integrated Quality and Productivity Performance Evaluation
35(11)
2.4.1 Interactions between Quality Behavior and Production Throughput
35(1)
2.4.2 Step 1: Quality Propagation
36(2)
2.4.3 Step 2: Multistage Overlapping Decomposition
38(2)
2.4.4 Iteration Procedure
40(3)
2.4.5 Convergence
43(1)
2.4.6 Accuracy
43(1)
2.4.7 Conservation of Flow
44(2)
2.5 Bottleneck Analysis
46(4)
2.5.1 DT-BN Indicator
47(1)
2.5.2 QBN Indicators
48(2)
2.6 Conclusions
50(1)
Acknowledgment
51(1)
Appendix A: Operators Theta(·), Phi1(·)and Phi2(·)
51(1)
References
52(5)
3 Event-Based Modeling For Battery Manufacturing Systems Using Sensor Data
57(22)
Qing Chang
Yang Li
Stephan Biller
Guoxian Xiao
3.1 Introduction
57(1)
3.2 Sensor Networks for Battery Manufacturing System
58(2)
3.3 Event-based Modeling Approach
60(8)
3.3.1 Market Demand-Driven System Description
60(3)
3.3.2 EBM for Market Demand-Driven Battery Manufacturing
63(1)
3.3.3 The Impact of Stations and Supporting Activities to the Overall System
64(4)
3.4 Event-based Diagnosis for Market Demand-Driven Battery Manufacturing
68(8)
3.4.1 Event-based Indicators on Market Demand-Driven System
69(5)
3.4.2 Bottleneck Analysis for Critical Stations
74(2)
3.5 Event-based Costing for Market Demand-Driven Battery Manufacturing System
76(1)
3.6 Conclusions
77(1)
Acknowledgment
78(1)
References
78(1)
4 A Review On End-Of-Life Battery Management: Challenges, Modeling, And Solution Methods
79(20)
Xiaoning Jin
4.1 Introduction
79(3)
4.1.1 Background
79(1)
4.1.2 Value Chain of EV Battery
80(1)
4.1.3 Why Remanufacturing?
80(2)
4.2 Research Issues of Battery Remanufacturing
82(6)
4.2.1 Remanufacturing Versus Traditional Manufacturing
82(1)
4.2.2 Remaining Useful Life Estimation
82(2)
4.2.3 Quality Variation of Battery Returns
84(1)
4.2.4 EOL Decision-Making for Battery Returns with Uncertainty
85(2)
4.2.5 Remanufacturing Processes
87(1)
4.2.6 Balancing Issue in Remanufacturing
87(1)
4.3 Modeling and Analysis for Battery-Remanufacturing Systems
88(6)
4.3.1 Modular Battery Testing, Rematching and Reassembly Issues
89(1)
4.3.2 Deteriorating Inventory Modeling
90(1)
4.3.3 Remanufacturing Strategies
91(2)
4.3.4 Reassembly Strategy with Quality Variation
93(1)
4.4 Summary
94(1)
References
94(5)
5 An Analytics Approach For Incorporating Market Demand Into Production Design And Operations Optimization
99(30)
Chris Johnson
Bahar Biller
Shanshan Wang
Stephan Biller
5.1 Introduction
99(2)
5.2 Design and Operational Decision Support
101(3)
5.3 Linkage to a Financial Transfer Function
104(6)
5.4 A Quantification of Risk in Design and Operations
110(3)
5.5 Exploration of Design and Operations Choices
113(5)
5.5.1 Market Coupling: Demand as Exogenous
113(3)
5.5.2 Market Coupling: Demand as Endogenous
116(2)
5.6 Manufacturing Operations Transfer Function: Throughput, Inventory, Expense, and Fulfillment
118(2)
5.7 Activity-based Costing
120(3)
5.8 Conclusion
123(1)
References
124(5)
Part II Battery Service Systems
6 Prognostic Classification Problem In Battery Health Management
129(22)
Junbo Son
Raed Kontar
Shiyu Zhou
6.1 Introduction
129(3)
6.2 Failure Predictions by Logistic Regression and JPM
132(4)
6.2.1 Failure Prediction by Logistic Regression
132(1)
6.2.2 Failure Prediction by JPM
133(3)
6.2.3 Summary of Logistic Regression and JPM Prognostic Frameworks
136(1)
6.3 Numerical Study
136(7)
6.3.1 Simulation Procedure
136(2)
6.3.2 Performance Evaluation
138(5)
6.4 Discussion of the Impact of Imbalanced Data
143(3)
6.5 Conclusion
146(1)
References
147(4)
7 A Bayesian Approach To Battery Prognostics And Health Management
151(24)
Bhaskar Saha
7.1 Introduction
151(1)
7.2 Background
152(2)
7.3 Battery Model for a Bayesian Approach
154(2)
7.4 Particle Filtering Framework for State Tracking and Prediction
156(4)
7.5 Battery Model Considerations for PF Performance
160(7)
7.5.1 Model 1: Exponential Approximations
160(1)
7.5.2 Electric UAV BHM Application
161(2)
7.5.3 Model 2: Logarithmic Approximation to Butler-Volmer Equation
163(1)
7.5.4 Model 3: Factoring in Parameter Dependence on Load Current
164(3)
7.6 Decision Making for Optimizing Battery Use
167(4)
7.6.1 Stochastic Programming for Optimizing Battery Life
168(2)
7.6.2 Optimization Strategy
170(1)
7.7 Summary
171(1)
References
172(3)
8 Recent Research On Battery Diagnostics, Prognostics, And Uncertainty Management
175(42)
Zhimin Xi
Rong Jing
Cheol Lee
Mushegh Hayrapetyan
8.1 Introduction
175(2)
8.2 Battery Diagnostics
177(9)
8.2.1 Battery Models
177(5)
8.2.2 Battery SOC and SOH Estimation
182(4)
8.3 Battery Prognostics
186(9)
8.3.1 Prognosis Algorithms
186(2)
8.3.2 Prognosis of Battery SOC and SOH
188(7)
8.4 Uncertainty Management
195(12)
8.4.1 Model and Parameter Uncertainties
196(1)
8.4.2 Battery Performance Estimation Under Uncertainties
197(2)
8.4.3 Case Study of Battery SOC Estimation Under Uncertainty
199(8)
8.5 Summary
207(1)
References
208(9)
9 Lithium-Ion Battery Remaining Useful Life Estimation Based On Ensemble Learning With LS-SVM Algorithm
217(16)
Yu Peng
Siyuan Lu
Wei Xie
Datong Liu
Haitao Liao
9.1 Introduction
217(1)
9.2 LS-SVM Algorithm
218(2)
9.3 LS-SVM Ensemble Learning Algorithm
220(4)
9.3.1 Data Collection and Preprocessing
221(1)
9.3.2 Input Vector Construction and Hyperparameter Determination
221(2)
9.3.3 LS-SVM Ensemble Learning Model Construction and Prediction
223(1)
9.3.4 Uncertainty Representation of RUL Prediction
223(1)
9.3.5 Performance Evaluation of RUL Prediction Algorithm
223(1)
9.4 Experiment Verification and Analysis
224(2)
9.4.1 Experimental Setting
224(1)
9.4.2 Experimental Results and Comparative Analysis
224(2)
9.5 Conclusion
226(3)
References
229(4)
10 Data-Driven Prognostics For Batteries Subject To Hard Failure
233(24)
Qiang Zhou
Jianing Man
Junbo Son
10.1 Introduction
233(3)
10.2 The Prognostic Model
236(9)
10.2.1 Assumptions and the Prognostic Framework
236(1)
10.2.2 Modeling
236(2)
10.2.3 Model Parameter Estimation
238(1)
10.2.4 Estimating the Survival Function of an In-service Battery
239(2)
10.2.5 Prognostics for an In-service Battery
241(2)
10.2.6 Extension to Degradation with a Change Point
243(2)
10.3 Simulation Study
245(6)
10.4 Summary
251(1)
References
252(5)
Part III Battery Management Systems (BMS)
11 Review Of Battery Equalizers And Introduction To The Integrated Building Block Design Of Distributed BMS
257(24)
Ye Li
Yehui Han
Liang Zhang
11.1 Concept of Battery Equalization
257(1)
11.2 Equalization Methods
258(6)
11.2.1 Passive Equalization
258(1)
11.2.2 Series Active Equalization
259(1)
11.2.3 Parallel Active Equalization
259(1)
11.2.4 Switched-Capacitor Equalization
260(1)
11.2.5 Transformer-Based Equalization
261(1)
11.2.6 Modularized Charge Equalization
261(1)
11.2.7 Other Equalizer Topologies
262(2)
11.3 Introduction of Integrated Building Block Design of a Distributed BMS
264(1)
11.4 The Proposed Integrated Building Block Design of BMS
264(4)
11.5 System Implementation
268(2)
11.5.1 Converter Controller Design
269(1)
11.5.2 Battery Monitoring
270(1)
11.5.3 Communication
270(1)
11.5.4 Control and Communication of the Proposed Architecture
270(1)
11.6 Tested System Description
270(3)
11.7 Functional Performance Evaluation
273(3)
11.8 Conclusion
276(1)
References
277(4)
12 Mathematical Modeling, Performance Analysis And Control Of Battery Equalization Systems: Review And Recent Developments
281(22)
Weiji Han
Liang Zhang
Yehui Han
12.1 Introduction
281(1)
12.2 Modeling of Battery Equalization Systems
282(7)
12.2.1 Circuit Analysis-based Modeling
283(2)
12.2.2 System Analysis-based Modeling
285(3)
12.2.3 Computer Simulation-based Modeling
288(1)
12.2.4 Future Topics
288(1)
12.3 Performance Evaluation of Battery Equalization Systems
289(3)
12.3.1 Circuit Analysis-based Approach
289(1)
12.3.2 System Analysis-based Approach
290(1)
12.3.3 Hardware Experiment and Computer Simulation
291(1)
12.3.4 Future Topics
292(1)
12.4 Control Strategies for Battery Equalization Systems
292(5)
12.4.1 Functionality-based Simple Control
292(2)
12.4.2 Heuristics-based Control
294(1)
12.4.3 Fuzzy Logic-based Control
294(1)
12.4.4 Model Predictive Control
295(1)
12.4.5 Optimal Control
296(1)
12.4.6 Future Topics
296(1)
12.5 Summary
297(1)
References
298(5)
13 Review Of Structures And Control Of Battery-Supercapacitor Hybrid Energy Storage System For Electric Vehicles
303(16)
Feng Ju
Qiao Zhang
Weiwen Deng
Jingshan Li
13.1 Introduction
303(1)
13.2 Batteries for EVs
304(1)
13.3 Supercapacitors for EVs
305(1)
13.4 Battery-Supercapacitor Hybrid Energy Storage System
306(6)
13.4.1 Passive HESS
308(1)
13.4.2 Active HESS
309(3)
13.5 Control Strategy for HESS
312(3)
13.5.1 Control without Demand Prediction
312(1)
13.5.2 Control with Demand Prediction
313(2)
13.6 Conclusions
315(1)
References
315(4)
14 Power Management Control Strategy Of Battery-Supercapacitor Hybrid Energy Storage System Used In Electric Vehicles
319(36)
Qiao Zhang
Weiwen Deng
Jian Wu
Feng Ju
Jingshan Li
14.1 Introduction
319(1)
14.2 Low-Level Hybrid Topologies
320(3)
14.3 High-Level Supervisory Control
323(27)
14.3.1 Modeling of Battery and Supercapacitor
324(3)
14.3.2 Time Domain Control
327(9)
14.3.3 Frequency Domain Control
336(3)
14.3.4 Integrated Power Management Strategy
339(11)
14.4 Conclusions
350(1)
References
351(4)
15 Federal And State Incentives Heighten Consumer Interest In Electric Vehicles
355(26)
William Canis
15.1 Introduction
355(1)
15.2 Electric Vehicles and the Federal Role
356(2)
15.3 Public Interest in HEVs and Electric Vehicles
358(2)
15.4 Federal Support for HEVs and Electric Vehicles
360(3)
15.5 Support for EVs in the Obama Administration
363(3)
15.6 Impact of GHG Regulations
366(2)
15.7 Vehicle Environmental Life Cycle Comparisons
368(1)
15.8 State Initiatives
369(4)
15.8.1 Direct and Indirect Subsidies
370(1)
15.8.2 ZEV Program
371(1)
15.8.3 Utility Incentives
372(1)
15.8.4 Fleet Vouchers
372(1)
15.8.5 Manufacturers' Incentives
372(1)
15.8.6 Research and Development
373(1)
15.8.7 Dealer Franchise Policies
373(1)
15.9 Prospects for Growth
373(3)
15.9.1 Battery Cost
373(1)
15.9.2 Vehicle Cost
374(1)
15.9.3 Charging
374(1)
15.9.4 Range
374(1)
15.9.5 Price of Gasoline
374(1)
15.9.6 Battery Safety Issues
375(1)
15.9.7 Advanced Technology Use in Gasoline Vehicles
375(1)
15.9.8 Other Government Subsidies
376(1)
15.10 Conclusion
376(1)
Acknowledgment
376(1)
References
376(5)
Index 381
JINGSHAN LI is a Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison, USA. He received his PhD in Electrical Engineering - Systems at the University of Michigan, USA.

SHIYU ZHOU is a Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison, USA. He received his PhD in Mechanical Engineering at the University of Michigan, USA.

YEHUI HAN is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison, USA. He received his PhD in Electrical Engineering at the Massachusetts Institute of Technology, USA.
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